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Dr Igor Khovanov

Developing efficient fluidic energy harvester

The idea of energy harvesting consists in a transformation of an external (ambient) energy of one type, for example mechanical vibrations or heat, to an electrical form. Energy harvesting appears to be an attractive alternative to traditional approaches of electricity production. A typical energy harvester consists in two interconnected parts: one mechanical produces energy, for example vibrations, and another electrical, as rule piezoelectric materials that produces electricity in a response to an applied stress. The latter part does not depend, in wide extend, on the source of ambient energy, whereas a structure of the former is defined by ambient energy.

In this project a harvester converting wind energy to electricity via producing mechanical vibrations and following piezoelectric conversion is developing and optimized to have a maximal efficiency for different wind conditions. Different wind speeds as well as turbulent and laminar wind flows will be considered and the harvester has to produce a stable output despite the wind conditions.

The key for an efficient harvester is comprehensive understanding wind-body interaction. The project assumes the development a model of wind-body interaction that is able to predict an optimal configuration of the harvester and further the design of a proto-type of the harvester.

Nonlinear Identification of stochastic engineering systems

This area can be considered as an extension of the classical engineering techniques used for identification and control.

The recognition that the nonlinearity and stochasticity are important components of system dynamics is growing in engineering. There are many well studied examples where nonlinearity and fluctuations are responsible for a non-trivial system response, however most of the examples are from fundamental research of biological and/or physical models, whereas only few examples are coming from engineering. Therefore we aim to provide a bridge from fundamental research of stochastic systems to engineering applications. This activity leads to new approaches and techniques for identification of models of engineering systems, for example civil structures; for prediction of system behaviour and formulating ways for system control.

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